Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

Anwar, Sajid; Al-Obeidat, Feras; Tubaishat, Abdallah; Din, Sadia; Ahmad, Awais; Khan, Fakhri Alam; Jeon, Gwanggil; Loo, Jonathan

doi:10.1109/jiot.2019.2954919

Cited by 20 publications

(7 citation statements)

References 21 publications

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“…In these attackers, the attackers collude negative feedback and cause of deteriorate the smart grid system performance. 4 Data attack In these attackers, the attackers manipulate, alter or delete the data and mislead the smart grids for wrong decisions. 5…”

Section: Bad Mouthing Attackmentioning

confidence: 99%

“…Smart grids systems should be able to defend their information from unauthorized access, disruption, inspection, modification, and disclosure. All data and communication processes should be integrated, confidential, non-repudiation from attackers and outside world [4,33]. Although, various models and frameworks have been proposed to secure the smart grid communication process still these networks are suffered from security and privacy issues.…”

Section: Introductionmentioning

confidence: 99%

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A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

Qureshi

Islam

Jeon

2021

AIS

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New technologies and automation systems have changed the traditional smart grid systems into new and integrated intelligent systems. These new smart systems are adopted for energy efficiency, demand and response, management and control, fault recovery, reliability and quality of services. With various benefits, smart grids have vulnerabilities due to open communication systems, and open infrastructures. Smart grids systems are based on real-time services, where privacy and security id one of the major challenge. In order to address these challenges and deal with security and privacy issues, we proposed a Trust Evaluation Model for Smart Grids (TEMSG) for secure data aggregation in smart grids and smart cities. This model tackles privacy and security issues such as data theft, denial of services, data privacy and inside and outside attacks and malware attacks. Machine learning methods are used to gather trust values and then estimate the imprecise information to secure the data aggregation in smart grids. Experiments are conducted to evaluate and analyze the proposed model in terms of detection rate, trustworthiness, and accuracy.

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Section: Bad Mouthing Attackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

Qureshi

Islam

Jeon

2021

AIS

Self Cite

View full text Add to dashboard Cite

show abstract

“…U, W, and V represent weight matrices. e output value O t is calculated in equation ( 1), and the state value S t is calculated in equation (2).…”

Section: Bidirectional Recurrent Neural Networkmentioning

confidence: 99%

“…However, mixing well-known URLs with malicious URLs to cause user confusion and achieve intrusion attacks on the host is one of the most common attack methods. At present, malicious URLs are detected by using rule matching and the black-and-white list [2,3]. But these methods are excessively dependent on the knowledge breadth of security personnel, which increases the possibility of false blocking of malicious URLs.…”

Section: Introductionmentioning

confidence: 99%

“…To solve these problems, scholars at home and abroad have done a lot of research studies. Anwar et al [2] and Li et al [4] proposed a method combining linear and nonlinear spatial transformation for URL recognition and detection. e method significantly improves the accuracy of URL recognition and detection using a support vector machine and neural network.…”

Section: Introductionmentioning

confidence: 99%

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Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model

Chen

Liu

Chen

et al. 2021

Security and Communication Networks

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The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.

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Defending the Digital Frontier: URL-Based Phishing Detection Extension

Vamsi,

Muthaiah,

Roshan Vardhan

2024

IFIP Advances in Information and Communication Technology

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Countering Malicious URLs in Internet of Things Using a Knowledge-Based Approach and a Simulated Expert

Cited by 20 publications

References 21 publications

A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

A trust evaluation model for secure data aggregation in smart grids infrastructures for smart cities

Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model

Defending the Digital Frontier: URL-Based Phishing Detection Extension

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